Effect Of Heavy Vehicle Weights On Pavement Performance
Chhote L. Saraf, George 1. lives, and Kamran Majidzadeh
Resource International. Inc .• USA
ABSTRACT
A study was conducted to determine the effect of heavy
vehicle weights on the performance of rigid. flexible and
composite pavements. Detailed traffic measurements were
made twice a year for two years using weigh-in-motion
equipment. Distress measurements. consisting of cracking.
faulting. Mays roughness, and PSI for rigid pavements and
cracking, rutting, Mays roughness, and PSI for flexible and
composite pavements, were made at the same time. These
measurements were analyzed to determine the effect of heavy
axle loads on measured distresses. Dynaflect deflections were
taken four (4) times during the monitoring period; the analysis
of this data showed only minor deterioration of pavement's
structural strength.
The analysis of data showed that for rigid pavements,
heavy axle loads may contribute toward cracking and faulting
development, whereas, rutting is most influenced by heavy axle
loads for flexible and composite pavements. Different load
equivalency factors for each distress type are, therefore,
required for estimating the effect of heavy vehicles on the
performance of pavements.
INTRODUCTION
Load equivalency factors currently used to convert mixed
traffic into 18,000 lb. single axle loads (E-18) were developed
from AASIITO Road data collected in 1959-60. The design of
heavy vehicles, their tire pressures and weights have changed
since that time. Therefore, the equivalent single axle loads
estimated from current load equivalency factors may not be
able to predict the performance of pavements accurately.
Keeping this in mind, a study to determine the effect of heavy
vehicles on the performance of pavements was sponsored by
the Ohio Department of Transportation (ODOT) and The
Federal Highway Administration (FHWA) in 1985. The
results of this study were published in a report submitted to
ODOT in 1991 [1]. The data collected for this study was
analyzed to determine the effect of heavy vehicle weights on the
performance of flexible, composite and rigid pavements. The
results of data analysis along with other relevant information
are described in this paper.
SITE SELECTION
The Ohio special Permit data for overloaded vehicles
showed that the weight limits for trucks traveling from
neigh boring states, such as Michigan, to northern Ohio cities
are substantially heavier than the loads permitted in Ohio.
Therefore, four sites were selected for the study near Toledo,
Ohio where these heavy vehicles use the roadways. The
selected sections were approximately Yz mile long and included
all three different types of pavements, viz., flexible, composite
and rigid. The data in Table 1 lists the locations and some
important features of each site. All sites are located in Lucas
County of Ohio.
FlELDDATA
The following field data was collected for this study:
1. Traffic,
2. Rutting measurements,
3. Faulting measurements,
4. Cracking measurements,
5. Roughness using Mays meter and
K.J. Law non-contact profilometer, and
6. Dynaflect deflection measurements.
A brief description of data collection method and the data
collected is as follows. The traffic lanes were numbered 1-4 for
the 4-lane divided highways.
According to ODOT
conventions, lane 1 is the driving lane of south or west bound
traffic and lane 4 is the driving lane of north or east bound
traffic.
Road transport technology-4. University of Michigan Transportation Research Institute, Ann Arbor, 1995.
253
ROAD TRANSPORT TECHNOLOGY-4
Table 1. Features of site selected for the study (all sites in Lucas County)
Feature
Site #1
Site #2
Site #3
Site #4
Location
1-475
US-23
1-75
1-280
Approx. limits, from mile post - mile post
6.80-7.20
10.90-1l.40
7.00~7.50
4.20-4.65
No. of lanes! directions
41N0rth and
South Bound
2!South Bound
only
4lEastand
WestBound
2!South Bound
only
Pavement type
Aexible
Comp., reinf.
Rigid, reinf.
Comp., reinf.
Joint Spacing
~
60'
40'
60'
Pavement layer thicknesses, inches
10" AC
4"Agg
2.5" AC
9" Conc
6"Agg
9" Conc
6"Agg
3.25" AC
9" Conc
6"Agg
Subgrade (ODOT Class)
A-4B
A-3
A-6
A-6
1. TRAFFIC DATA
Traffic data was collected with the help of a weigh-inmotion (WIM) equipment. A preliminary study of available
WIM equipment indicated that the Golden Weighman (1M)
could be used to meet the traffic analysis needs of this study.
This WIM system consists of a capacitive weighmat to sense
the axle loads (only ¥2 of an axle is measured and this
measurement is doubled to get the axle load) and two inductive
loops that act as axle detectors. The loops were installed in
grooves cut into the pavement surface (about I inch deep) and
functioned well over the 18 month monitoring period.
At the outset of the project it was felt that at least three (3)
days of traffic measurements per project would be needed to
get an accurate estimate of the traffic mix and that each project
should be monitored twice per year. Since one working day
would be required for system installation and distress
measurements, and allowing for bad weather (ODOT policy
was not to close off lanes during wet weather, nor could the
weighmat be installed when the pavement was wet), it was
decided to only. monitor one project per week. The general
procedure was to install the WIM equipment on Wednesday,
start the recording at 4 p.m. and continue collecting data until
8 p.m. on the following Tuesday; it was necessary to stop
recording the night before in order to recharge the Weighman
(1M) batteries. This period was chosen because traffic in the
Toledo area is very similar on Tuesdays and Wednesday, and
to some extent also on Thursdays. However, this pattern could
not always be followed due to wet weather, equipment
malfunction and on a few occasions, the availability of the
traffic control crew.
The field monitoring had to be conducted between April
1 and October 31 each year. These dates were selected
because studded snow tires were legal on Ohio highways
between November 1 and March 31 and the WIM weighmat
cannot long survive under studded tires. Further, the weighmat
is temperature sensitive; it is temperature compensated for
temperatures above freezing but not well compensated for
254
temperatures below freezing. Therefore, April through
October period represents the practical time span available for
monitoring. The system was calibrated before using it at the
study sites.
The data collected was analyzed and stored in the
Weighman (1M) which is programmed to retain data in various
modes using a programmer/retriever which also acts as an
interface with a computer. The data measured included vehicle
speed, load and length, axle load, spacing and type (steering,
single, multiple) and time of arrival. All this data could be
stored but this is impractical since in this configuration the
128k memory would be filled in a few hours; furthermore, such
detail is not necessary for most purposes. The data storage
mode selected was that which segregated the vehicles by the
fHWA vehicle classification scheme F, and axles by type
(steering, single, multiple), as well as storing axle weights in
twelve (12) user-defined weight bins. Gross vehicle weight was
also categorized into twelve bins (whose limits are fixed at four
(4) times that of the axle load bins). [2] A recording interval
of four (4) hours was selected.
The results of the traffic measurements are shown in Table
2. In this table the day factor (this factor converts traffic
measurements made on a specific day into ADT values) has
been derived from ODOTs permanent traffic counting stations
that have been operational for several years in the Toledo area
(although not at the same locations). Type C vehicles represent
medium weight trucks belonging to FHWA Classes 4-7, Type
B vehicles represent heavy trucks in Classes 8-13 and Class 13
vehicles (7 or more axles, mUlti-units) represent the "Michigan
Train. "[2] The road identification consists of: Road No.Lane.Monitoring Period, e.g. 475-1.2 represents 1-475, Lane 1,
and Monitoring Period 2.
The average traffic volume measurements (ADT/lane)
shown in Table 2 were in general quite accurate and that
vehicle classification (at least as far as Type B and Type C
vehicles are concerned) was also satisfactory, especially when
PAVEMENT PERFORMANCE
Table 2. Summary of traffic survey data
Class 13 Vehicles
Length,
Days
Road
Number
Day
Factor
ADTI
Lane
No. of C
TruckslDay
No. of B
TruckslDay
No. Per
Day
% Over
80 KIP
475-1.1
475-1.2
475-1.3
475-1.4
1.0
6.2
6.2
6.0
1.03
0.99
0.91
0.85
10887
11372
12952
9815
186
241
209
334
1623
1350
947
945
32
11
6
83
50
69
97
37
475-2.1
475-2.2*
475-2.3*
475-2.4
3.7
6.0
3.3
6.0
1.04
0.91
0.95
0.85
5134
6637
6626
6615
25
49
142
31
254
301
459
196
1
16
63
1
25
8
20
16
475-3.1
475-3.2
475-3.3
475-3.4
5.2
0.7
0.5
6.0
0.99
0.85
0.90
0.99
7711
6273
6456
8026
42
91
158
61
415
357
301
307
2
33
36
17
64
4
11
3
475-4.1
475-4.2
475-4.3
475-4.4
4.8
6.2
5.3
6.0
0.90
0.99
0.99
0.85
11341
12058
11907
11843
202
327
252
246
1144
1431
1521
1048
8
152
15
54
16
4
30
18
23-1.1
23-1.2
23-1.3
23-1.4
5.0
7.2
5.7
6.0
1.03
0.85
0.90
0.99
8962
8267
7885
7928
147
152
121
163
1208
893
793
713
16
. 12
9
24
51
66
65
28
23-2.1
23-2.2
23-2.3
23-2.4*
4.5
6.2
5.8
1.2
1.04
0.85
0.91
0.86
5806
6085
7379
4690
196
108
110
354
536
703
846
657
6
10
28
60
27
45
58
34
75-1.1
75-1.2
75-1.3
Equipment
5.3
4.0
Malfunctll.
0.89
0.83
10809
12476
749
1447
975
288
19
4
23
75-2.1
75-2.2
75-2.3
6.8
6.2
6.0
0.86
1.02
0.86
7338
7418
6882
282
300
296
176
289
138
2
3
2
13
47
17
75-3.1
75-3.2
75-3.3
75-3.4
6.7
6.2
6.0
0.90
0.99
0.98
11540
9686
9772
234
218
238
1655
1608
1066
11
16
9
53
49
50
75-4.1
75-4.2
75-4.3
4.8
6.2
6.0
0.85
0.99
0.90
6378
7222
6715
173
298
479
315
626
563
6
3
3
25
48
75
280-1.1
280-1.2
6.5
6.2
0.86
1.01
12770
13746
425
357
1486
1887
191
12
14
56
280-2.1
280-2.2
5.7
6.7
0.85
1.01
8516
9071
293
100
703
832
86
4
2
33
17
*Weighmat failed
255
ROAD TRANSPORT TECHNOLOGY-4
monitoring times were greater than 4 days. However, small
discrepancies were noted in classifying vehicles. For
instance, it was noticed during visual cross-checks that the
equipment tended to mis-classify vehicles when two vehicles
traveled close together (one after the other). This is
especially true for Class 13 vehicles where two vehicles with
a combined total of more than 6 axles were classified as one
Class 13 vehicle. In most cases where a high number of
Class 13 vehicles has been found, the percent of these
vehicles weighing over 80 kips is low, indicating a high
probability of misclassification. The Weighmat on 1-280
was located just upstream from a draw bridge; consequently
traffic tends to move close together during the times when
the draw bridge was operated. This most probably explains
why 1-280 has a significant variation in the number of Class
13 vehicles accompanied by a significant change in the
percentage of heavy Class 13 vehicles.
2. RUTTING MEASUREMENTS
The extent of rutting was measured in both wheel paths
at 100 feet (3Om) intervals using a 7 feet straight edge and a
combination square. The location of maximum rut depth
was determined by sight and measured to the nearest lI64th
of an inch. Care was taken to place the straight edge so that
it was not on the painted edge lines as the wear in these
cause measurements error. Measurements were always
taken at the same locations; the pavement was marked with
spray paint to ensure this. Average rutting measurements of
all sites are listed in Table 3.
Table 3. Average rutting and total cracking measurements, Project site 1-475, US-23 and 1-280
256
Date
No. of 18-kip Load
Applications
Total Cracking
Route Number
(Ft.)
Average Rutting
(In.)
1-475-1.1
1-475-1.2
1-475-1.3
1-475-1.4
07/05/86
20108/86
02106/87
26/08/87
5,695,480
5,872,022
6,415,360
6,454,782
414
412
466
651
0.126
0.126
0.095
0.133
1-475-2.1
1-475-2.2
1-475-2.3
1-475-2.4
14/04/86
28/08/86
05105/87
19/08/87
1,346,896
1,400,684
1,501,836
1,538,754
632
637
668
755
0.079
0.064
0.074
0.085
1-475-3.1
1-475-3.2
1-475-3.3
1-475-3.4
22/04/86
10109/86
15104/87
12/08/87
1,412,708
1,478,562
1,583,546
1,624,585
499
497
534
621
0.081
0.058
0.070
0.097
1-475-4.1
1-475-4.2
1-475-4.3
1-475-4.3
29/04/86
03109186
06/04/87
05/08/87
4,160,056
4,394,044
4,805,410
4,934,544
147
152
188
245
0.130
0.141
0.145
0.167
US-23-1.2
US-23-1.2
US-23-1.3
US-23-1.4
01/04/86
12108/86
14/07/87
03/09/87
1,456,785
1,582,770
1,914,417
1,963,417
736
739
738
744
0.108
0.098
0.103
0.103
US-23-2.1
US-23-2.2
US-23-2.3
US-23-2.4
02104/86
28/07/86
16/06/87
10/09/87
1,176,784
1,267,017
1,522,085
1,590,797
686
703
682
686
0.120
0.115
0.105
0.114
1-280-1.2
1-280-1.2
22/07/86
15110/86
10,412,966
10,619,220
612
626
0.337
0.310
1-280-2.1
1-280-2.2
16/07/86
22110/86
2,945,354
3,012,708
578
625
0.268
0.318
Table 4. Total cracking and average faulting measurements, Project site 1-75
Route Number
Date
No. Of 18-kip Load
Applications
Total Cracking
(Ft.)
Average Faulting
(In.)
1-75-1.1
1-75-1.2
1-75-1.3
1-75-1.4
15107/86
02110/86
21107/87
09109/87
7,367,617
7,556,544
8,277,903
8,388,315
1,588
1,857
1,884
1,920
0.081
0.090
0.074
0.090
1-75-2.1
1-75-2.2
1-75-2.3
1-75-2.4
07/07.86
10110/86
21107/86
09/09/87
1,566,081
1,605,746
1,727,725
1,746,917
1,343
1,423
1,444
1,464
0.011
0.016
0.032
0.013
1-75-3.1
1-75-3.2
1-75-3.3
1-75-3.4
24/06/86
24/09/86
14/07/87
23/09/87
6,699,692
6,859,982
7,367,567
7,502,923
1,409
1,423
1,446
1,506
0.065
0.070
0.072
0.069
1-75-4.1
1-75-4.2
1-75-4.3
1-75-4.4
01107/86
17/09/86
01107/87
23/09/87
3,768,668
3,844,668
4,133,668
4,212,668
1,408
1,471
1,488
1,644
0.068
0.081
0.089
0.076
3. FAULTING MEASUREMENTS
Faulting measUrementS were made on the outside edges of
the slab at about 12 inches in from the edge. The I 2 inch
distance was selected to be away from the painted edge lines
and also to avoid any excess joint filler and/or significant joint
spalling; however, measurements sometimes had to be shifted
slightly to clear the obstacles. A combination square was used,
with faulting values recorded to the nearest 1/64th of an inch;
every joint was measured. Average faulting measurement at
project site 1-75 are listed in Table 4.
4. CRACKING MEASUREMENTS
Cracking measurements consisted of estimating the length
of each crack to the nearest foot. To facilitate this, a sketch was
made of each project during the fitst survey showing the
location and approximate length of each crack. This allowed
the changes in cracking to be recorded on these figures during
subsequent surveys and resulted in much more accurate
estimate of the extent of cracking than would otherwise have
been possible. Total cracks measured at all sites are listed in
Tables 3 and 4.
5. ROUGHNESS MEASUREMENTS
Pavement roughness was measured using a Mays Meter
mounted on a midsize car and also by a Kl. Law non-contact
profilometer, which provided PSI values. Measurements were
always made over the entire project length; the start and end
points of each project were painted on the sides of the road for
easy visibility. The roughness measurements are summarized
in Table 5.
6. DYNAFLECT DEFLECTION MEASUREMENTS
Dynaflect deflection measurements were made by ODOT
at about 100 feet intervals on all lanes of 1-475 (flexible
pavement) and at about 50 feet (15 m) intervals on the south
bound lanes of US 23 (composite pavement); no measurements
were made at joint locations because very few joints had
reflected through the overlay. Measurements were made at
each joint and at each location for all four lanes ofI-75 and for
the southbound lanes of 1-280. The joint measurements
consisted of "approach" and "leave" measurements. In the
approach case the loading wheels and number 1 sensor are
placed about 6 inches (150 mm) on the upstream slab, with the
remainder being on the downstream slab, and in the leave case
all sensors are on the downstream slab with the loading wheels
being about 6 inches (150 mm) downstream from the joint.
Air and pavement surface temperature were also recorded,
along with weather information. Due to limited space in this
paper, deflection data is not included here. The results of data
analysis, however, are discussed later in this paper.
ANAL YSIS OF DATA
The data collected for this study was analyzed to determine
the effect of heavy loads on various pavement performance
parameters measured for this study. For this purpose, the
traffic data was anaIyzed to estimate the total number of 18-kip
single axle load applications (E-18) for each lane of the road
section using the conventional load equivalency factors. The
estimatednumberofE-18 are listed in Tables 3, 4 and 5 along
with the performance measurements. The WIM data listed in
Table 2 was used to estimate the averages of ADT and number
of B, C and Class 13 trucks for each lane of the study section.
The results of this analysis are summarized in Table 6. These
traffic data were used to relate the performance measurements
with the number ofE-18 and/or heavy trucks (Class 13). The
following paragraphs describe the analysis of data and the
results.
257
ROAD TRANSPORT TECHNOLOGY-4
Table 5. Summary of roughness measurements.
258
Route
Number
Date
No. of 18-kip
Load AppL
Mays, in}
0.2 mile
1-475-1.1
1-475-1.2
1-475-1.3
1-475-1.4
18/08/86
16112186
31108/87
10/02188
5,868,594
6,026,282
6,463,352
6,597,809
1-475-2.1
1-475-2.2
1-475-2.3
1-475-2.4
18/08/86
16112/86
31108/87
10/02/88
1-475-3. I
1-475-3.2
1-475-3.3
1-475-3.4
PSI
Date
No. of 18-kip
Load AppL
(OOOn
4.57
7.27
5.90
6.56
09/86
12186
02187
12187
03/88
5,914,872
6,024,568
6,178,828
6,669,032
6,657,799
3.74
4.02
3.63
3.57
3.70
1,393,046
1,441,214
1,543,571
1,607,795
5.50
5.96
6.80
7.12
09/86
12186
02187
12187
03/88
1,403,884
1,440,813
1,476,939
1,591,739
1,621,844
3.74
3.99
3.64
none
3.57
18/08/86
16112186
31108/87
10/02188
1,468,063
1,511,966
1,633,652
1,642,273
6.58
5.80
4.58
6.73
09/86
12186
02187
12187
03/88
1,480,948
1,511,488
1,554,436
1,690,916
1,658,975
3.48
3.97
3.47
none
3.57
1-475-4.1
1-475-4.2
1-475-4.3
1-475-4.4
18/08/86
16112186
31108/87
10/02188
4,275,981
4,502,421
4,983,606
5,285,526
6.25
9.15
5.19
7.55
09/86
12186
02187
12187
03/88
4,326,930
4,500,534
4,670,364
5,210,046
5,351,571
3.73
4.00
3.64
3.69
3.74
US-23-1.1
US-23-1.2
US-23-1.3
US-23-1.4
18/08/86
16112186
31108/87
10/02/88
1,618,488
1,705,180
1,960,329
2,125,279
4.80
6.80
5.09
4.53
09/86
12186
02187
12187
03/88
1,648,107
1,704,083
1,802,813
2,135,204
2,233,934
3.48
3.73
3.69
3.68
3.38
US-23-2.1
US-23-2.2
US-23-2.3
US-23-2.4
18/08/86
16112186
31108/87
10/02188
1,306,566
1,376,551
1,582,526
1,715,687
5.88
6.03
5.75
5.49
09/86
12186
02187
12187
03/88
1,329,114
1,375,716
1,450,875
1,703,910
1,779,069
3.56
3.91
3.66
None
3.52
1-75-1.1
1-75-1.2
1-75-1.3
1-75-1.4
1-75-1.5
18/09/86
16112186
31108/87
7,514,833
7,740,564
8,366,232
11.87
16.93
10.90
09/86
12186
02187
12187
03/88
7,507,472
7,738,111
7,885,327
8,628,767
8,849,591
2.82
3.05
2.81
2.82
2.80
1-75-2.1
1-75-2.2
1-75-2.3
1-75-2.4
1-75-2.5
18/09/86
16112186
31108/87
1,595,083
1,634,321
1,743,079
11.09
14.57
9.31
09/86
12186
02187
12187
03/88
1,593,804
1,663,895
1,659,485
1,788,714
1,827,099
3.28
3.54
3.42
none
3.40
1-75-3.1
1-75-3.2
1-75-3.3
1-75-3.4
1-75-3.5
18/09/86
16112/86
31108/87
6,843,953
7,007,805
7,461,960
13.47
17.49
11.27
09/86
12186
02187
12187
03/88
6,838,610
7,006,024
7,112,884
7,652,527
7,812,817
2.72
3.03
2.84
none
2.73
PAVEMENT PERFORMANCE
Table 5. Summary of Roughness Measurements (continued)
Route Number
Date
No. Of E-18
Mays
Date
1-75-4.1
1-75-4.2
1-75-4.3
1-75-4.4
1-75-4.5
18/09/86
16/12186
31/08/87
3,842,668
3,934,668
4,189,668
12.87
16.34
11.06
1-280-1.1
1-280-1.2
1-280-1.3
1-280-1.4
18/08/86
16/12186
10,550,088
10,767,455
1-280-2.1
1-280-2.2
1-280-2.3
1-280-2.4
18/08/86
16112186
2,988,221
3,049,788
No. Of E-18
PSI
09/86
12186
02187
12187
03/88
3,839,668
3,933,668
3,993,668
4,296,668
4,386,668
3.10
3.17
2.20
2.71
3.07
8.13
10.66
09/86
12186
02187
12187
10,621,368
10,764,815
11,002,415
11,802,335
3.37
3.51
3.38
3.64
6.27
8.76
09/86
12186
02187
12187
3,007,607
3,049,070
3,113,690
3,331,244
3.49
3.80
3.40
none
Table 6. Averages of traffic data listed in Table 2.
No. Of
ClDay
No. Of
BlDay
No. Of
Class 13IDay
Total Trucks
(B+C+
Class 13)lDay
Road
Lane
ADT/
Lane
1-475
1
2
3
4
11,257
6,253
7,117
11,787
243
62
88
257
1,216
303
345
1,282
33
20
22
57
1,492
385
495
1,596
US-23
1
2
8,261
5,990
146
192
902
686
15
26
1,063
904
1-75
I
2
3
4
11,643
7,213
10,333
6,771
1,098
293
230
317
632
201
1,443
501
12
2
12
4
1,742
496
1,685
822
1-280
1
2
13,258
8,794
391
197
1,687
768
102
45
2,180
1,010
1. ANALYSIS OF CRACKING DATA
The cracking data for flexible pavement (1-475 road
section) is listed in Table 3. This data indicates that total
cracking for each lane increased with time and number of E-18.
However, no clear trend was observed when total cracking data
was combined for all 4 lanes. Coincidentally, it was observed
that lane #4 developed the least amount of total cracking and
lane #2 developed the highest amount of total cracking (see
Table 3). Also, lane #4 carried the highest number of Class 13
trucks and lane #2 carried the least number of Class 13 truck
(see Table 6). The cracking for lanes 3 and 1 also follow this
trend. This indicated that E-18 obtained from load equivalency
factors may not be representative of cracking related
performance of flexible pavements.
Cracking data for rigid pavement (1-75) is shown in Table
4. This data indicated that cracking in each lane increased with
time and E-18. But, as was the case with flexible pavements,
no relation between E-18 and total cracking was observed
when data for all 4-lanes was combined. Presence of greater
number of Class 13 vehicles in lanes I and 3 did not cause
greater cracking in these lanes when compared with lanes 2
and 4.
Cracking data for composite pavements (1-280 and US-23
roadway segments) is listed in Table 3. This data shows that
total cracking in these pavements did not change significantly
during the observation period of approximately 17 months.
259
ROAD TRANSPORT TECHNOLOGY-4
There is a significant difference in the traffic (E-18 as well as
Class 13 vehicles) in lane I and lane 2 of route I-280.However,
no such difference in total cracking in these lanes was observed
for this pavement.
2. ANALYSIS OF RUTTING DATA
Rutting measurements were recorded for flexible and
composite pavements. The data for flexible pavement (1-475)
indicated that rutting in any given lane generally increased with
time andE-18. Among the two highest rutting lanes (1 and 4),
lane #4 rutted most but lane #1 carried the most E-18 (see
Table 3). A comparison with Class 13 data (see Table 6),
however, showed that lane #4 carried the most Class 13
vehicles and also rutted the most. A regression analysis of this
data (flexible pavement) was, therefore, performed to obtain a
relationship between the observed rutting (RUlF) and the
number of Class 13 vehicles (CI3) per day and the combined
Band C trucks (B&C). Another independent variable
(months) was added to this equation which represented the
month of testing. The count for month of testing started with
January 1986 as month = 1. Data collected during any given
month was recorded as a whole number. For example May
1986 was recorded as 5 months and June 1987 was recorded
as 13 months and so on. The equation derived from the data is
as follows:
RU1F =0.035 + 0.984 (CB) + 0.03 (B & C)
(1)
+ 0.0007 (months)
where,
RUIF Rutting in flexible pavement, in,
C 13 No. of Class 13 vehicles in the lane per day
in thousands,
B & C =Total number of B & C trucks combined per
day in thousands, and
months number of months since January, 1986 as
explained above.
The correlation coefficient (r) square of this data was 0.86.
However, the relationship is limited to the data range used and
it is not intended to be an universal equation. It is evident from
this equation that heavy vehicles (CI3) contribute significantly
more to rutting of flexible pavement than other trucks (B & C).
The coefficient for CB is about 33 times larger than the
coefficient for B&C.
The rutting data for composite pavements (US-23 and 1280) indicated that lane #1 of US-23 (see Table 3) carried
slightlymoreE-18 than lane #2. But the rutting in lane #1 was
slightly less than lane #2. A substantial difference in E-18 in
lane #1 and #2 of 1-280 showed only a slight difference
between the rutting in lane #1 and #2 of this road. On the other
hand, the summary of traffic data shown in Table 6 indicated
that lane #2 of US-23 carried slightly more C13 vehicles than
lane #1, which may explain slightly more rutting in lane #2
than lane #1. Also, the difference in rutting in lanes I and 2 of
1-280 is consistent with the slight difference in Cl3 vehicles in
these lanes rather than a significant difference between E-18
values listed in Table 3 for this road. These observations
indicate that rutting in composite pavements is also affected by
heavy vehicles(CI3).
=
=
=
260
3. ANALYSIS OF FAULTING DATA
Faulting data was collected for rigid pavement (1-75) only.
The data listed in Table 4 indicates that in any given lane,
faulting increased with time and E-I8. Also, when data for
lanes 1 and 2 is combined, there is a clear trend between the
faulting and E-18 due to significant difference between the E18 in these lanes. However, the difference in E-18 of lane #3
and #4 does not show similar trends. The number of C 13
vehicles listed in Table 6 for this route also do not indicate any
consistent trend with this parameter. However, relatively
higher percentage of Band C and Class 13 trucks in lanes I, 3
and 4 were observed to develop more faulting than lane 2
which carried lesser percentage of trucks (percent of
ADT/day). These percentages for lanes I, 3, 4 and 2 are 15,
16, 12 and 7 respectively.
These results indicate that faulting in rigid pavement is
affected by all types of trucks but the effect of heavy vehicles in
this case may have been shadowed by their small number in the
traffic mix.
4. ANALYSIS OF ROUGHNESS DATA
Roughness data was collected by two different devices;
Mays Meter and K.J. Law non-contact profilometer. The data
collected by these devices is summarized in Table 5. The
flexible pavement data (1-475) shows that the roughness
measured by May Meter increased with time and E-18 for any
given lane. However, no correlation could be found between
E-18 and May Meter roughness when data for all4-lanes was
combined together. This roughness did not show any
correlation with the number of heavy vehicles (CB) in
different lanes. The PSI data also did not correlate with either
E-18 or heavy vehicles (CB).
The data from two composite pavements (US-23 and 1280) showed some increase in Mays Meter roughness with
time and E-18 in only one case (1-280). However, the number
of heavy vehicles in individual lanes of both pavements
increased the roughness with increase in their numbers. PSI
did not show noticeable change in either case.
The rigid pavement roughness (1-75) did not show any
noticeable trend for either Mays Meter data or PSI data with E18 and CB traffic.
5. ANALYSIS OF DEFLECTION DATA
The deflection data collected for this study was analyzed
to determine the effect of heavy vehicles on this parameter.
Although, it was observed that the maximum deflection (WI)
generaIly increased during the study period, yet this increase in
(W 1) did not indicate significant deterioration in pavement
structurally. The total cracking in sections ofl-75, 1-280 and
1-475 changed more than the cracking in US-23. Therefore,
these sections may have undergone slight structural strength
change than US-23 section. This was evident from the
deflection data for US-23 section.
RESULTS OF DATA ANALYSIS
The results of data analysis (described in the previous
section) are as follows:
1. The presence of heavy vehicles in the traffic mix did
not alter the cracking of pavements of all three types.
2. The effect of heavy vehicles on rutting in flexible and
composite pavements was significant when compared
with other trucks (B & C) as indicated by Equation (1).
3. Faulting in rigid pavement did not show any significant
effect of Class 13 vehicles only. However, a greater
percentage of all trucks (B, C and Class 13) carried by
lanes 1, 3 and 4 ofI-75 section developed more faulting
in these lanes than lane #1 which carried lesser
percentage of all trucks.
4. Effect of heavy vehicles on roughness of pavements was
no different than the effect of other trucks (B&C) on
roughness.
5. The presence of heavy vehicles did not alter the trends
in structural deterioration of pavements as observed
from the deflection data. Slight deterioration in
structural strength of pavementS as observed from the
Dynaflect deflection data may be due to the presence of
cracks in these pavements
CONCLUSIONS
Based on the results of this study it was observed that in
general the traffic affected the performance (as measured by
various parameters) of all types of pavements. When data from
each lane of the roadway was considered, all distresses
increased with time andlor E-18. However, the presence of
different number of heavy vehicles in the mix of traffic in each
lane of a roadway, the effect of heavy vehicles on the
performance of pavement was not clearly delineated except in
case of rutting in flexible and composite pavements and to
some extent faulting in rigid pavement. Different load
equivalency factors (different than currently used) for different
pavement distresses are, therefore, required to determine the
effect of heavy vehicles on various pavement distresses.
ACKNOWLEDGMENT
The data used in this paper was obtained from a study
sponsored by the Ohio Department of Transportation (ODOT)
and the Federal Highway Administration (FHWA).
REFERENCES
1. George J. lives, K. Majidzadeh and A. Damoulakis,
"Reevaluation of the methods for calculations of load
equivalency and damage ratios", Resource International, Inc.
May, 1991.
2. Federal Highway Administration, "FHWA Vehicle
Classifications with Definitions", Exhibit 4-3-1, June, 1985.
261